Chain of Thought Prompting is a specific Prompt Engineering strategy that elicits step-by-step reasoning from a language model by including worked examples of intermediate reasoning in the prompt, or by appending an instruction such as "Let's think step by step." Rather than training a new model, it steers an existing one toward more deliberate, structured output at inference time. The technique is most effective on large models (typically above 100B parameters) and on tasks that require sequential deduction, arithmetic, or multi-hop reasoning.

Think of it like asking a colleague to talk you through their logic out loud before giving you a recommendation: the act of verbalizing each step surfaces assumptions and catches mistakes that a quick verbal answer would hide.

A few-shot CoT prompt pairs each example question with a chain of reasoning steps, then presents the target question. For a math problem, the model traces through the arithmetic and lands on the answer only after the reasoning is complete. Zero-shot variants omit worked examples and rely solely on instructional cues—surprisingly effective at GPT-4 scale.

Q: A train travels 60 mph for 2.5 hours. How far does it go?
A: The formula is distance = speed x time.
   60 x 2.5 = 150. The train travels 150 miles.

Q: A car travels 45 mph for 3 hours. How far does it go?
A: [model continues the reasoning pattern]

Chain of Thought Prompting connects directly to Zero-Shot reasoning, Self-Attention (which enables cross-step coherence), and Temperature (lower values keep reasoning chains consistent). It also underpins more advanced frameworks like Tree of Thought and ReAct agents. Hallucination rates often drop when a model must justify each step, though they do not disappear entirely.

You will encounter Chain of Thought Prompting in LLM API documentation, agent framework tutorials, and any research comparing model performance on standardized reasoning benchmarks.